INTERACTION-BASED SECURITY FOR MOBILE APPS

Mobile operating systems pervade our modern lives. Security and privacy is of particular concern on these systems, as they have access to a wide range of sensitive resources. Apps access these sensitive resources to help users perform tasks. However, apps may use these sensitive resources in a way that the user does not expect. For example, an app may look up reviews of restaurants nearby, but also leak the user’s location to an ad service every hour. I claim that interaction serves as a valuable component of security decisions, because the user’s interaction with the app’s user interface (UI) deeply informs their mental model of how apps access sensitive data. I introduce the notion of interaction-based security, wherein security decisions are driven by this interaction. To help understand and enforce interaction-based security, I present four pieces of work. The first is Redexer, which performs binary instrumentation of off-the-shelf Android binaries. Binary instrumentation is a useful tool for enforcing and studying security properties. I demonstrate one example of how Redexer can be used to study location privacy in apps. Android permissions constrain how data enters apps, but do not constrain how the information is used or where it goes. Information-flow allows us to formally define what it means for data to leak from applications, but it is unclear how to use information-flow policies for Android apps, because apps frequently declassify information. I define interaction-based declassification policies, and show how they can be used to define policies for several example apps. I then implement a symbolic executor which checks Android apps to ensure they respect these policies. Next, I test the hypothesis that the app’s UI influences security decisions. I outline an app study that measures when apps use sensitive resources with respect to their UI. I then conduct a user study to measure how an app’s UI influences their expectation that a sensitive resource will be accessed. I find that interactivity plays a large role in determining user expectation of sensitive resource use, and that apps largely access sensitive resources interactively. I also find that users may not always understand background uses of these sensitive resources and using them expectation requires special care in some circumstances. Last, I present a tool which can help a security auditor quickly understand how apps use resources. My tool uses a novel combination of app logging, symbolic execution, and abstract interpretation to infer a formula that holds on each per- mission use. I evaluate my tool on several moderately-sized apps and show that it infers the same formulas we laboriously found by hand

Higher-Order, Data-Parallel Structured Deduction

State-of-the-art Datalog engines include expressive features such as ADTs (structured heap values), stratified aggregation and negation, various primitive operations, and the opportunity for further extension using FFIs. Current parallelization approaches for state-of-art Datalogs target shared-memory locking data-structures using conventional multi-threading, or use the map-reduce model for distributed computing. Furthermore, current state-of-art approaches cannot scale to formal systems which pervasively manipulate structured data due to their lack of indexing for structured data stored in the heap. In this paper, we describe a new approach to data-parallel structured deduction that involves a key semantic extension of Datalog to permit first-class facts and higher-order relations via defunctionalization, an implementation approach that enables parallelism uniformly both across sets of disjoint facts and over individual facts with nested structure. We detail a core language, $DL_s$, whose key invariant (subfact closure) ensures that each subfact is materialized as a top-class fact. We extend $DL_s$ to Slog, a fully-featured language whose forms facilitate leveraging subfact closure to rapidly implement expressive, high-performance formal systems. We demonstrate Slog by building a family of control-flow analyses from abstract machines, systematically, along with several implementations of classical type systems (such as STLC and LF). We performed experiments on EC2, Azure, and ALCF's Theta at up to 1000 threads, showing orders-of-magnitude scalability improvements versus competing state-of-art systems

SymDroid: Symbolic Execution for Dalvik Bytecode

Apps on Google's Android mobile device platform are written in Java, but are compiled to a special bytecode language called Dalvik. In this paper, we introduce SymDroid, a symbolic executor that operates directly on Dalvik bytecode. SymDroid begins by first translating Dalvik into mu-Dalvik, a simpler language that has only 16 instructions, in contrast to Dalvik's more than 200 instructions. We present a formalism for SymDroid's symbolic executor, which can be described with a small number of operational semantics rules; this semantics may be of independent interest. In addition to modeling bytecode instructions, SymDroid also contains models of some key portions of the Android platform, including libraries and the platform's lifecycle control code. We evaluated SymDroid in two ways. First, we ran it on the Android Compatibility Test Suite, and found it passed all tests except ones that used library or system routines we have not yet implemented. On this test suite, SymDroid runs about twice as slow as the Dalvik VM, and about twice as fast as the Java VM. Second, we used SymDroid to discover the (path) conditions under which contacts may be accessed on an Android app, and found it was able to do so successfully. These results suggest that SymDroid, while still a prototype, is a promising first step in enabling direct, precise analysis of Android apps

Dr. Android and Mr. Hide: Fine-grained security policies on unmodified Android

Google’s Android platform includes a permission model that protects access to sensitive capabilities, such as Internet access, GPS use, and telephony. We have found that Android’s current permissions are often overly broad, providing apps with more access than they truly require. This deviation from least privilege increases the threat from vulnerabilities and malware. To address this issue, we present a novel system that can replace existing platform permissions with finer-grained ones. A key property of our approach is that it runs today, on stock Android devices, requiring no platform modifications. Our solution is composed of two parts: Mr. Hide, which runs in a separate process on a device and provides access to sensitive data as a service; and Dr. Android (Dalvik Rewriter for Android), a tool that transforms existing Android apps to access sensitive resources via Mr. Hide rather than directly through the system. Together, Dr. Android and Mr. Hide can completely remove several of an app’s existing permissions and replace them with finer-grained ones, leveraging the platform to provide complete mediation for protected resources. We evaluated our ideas on several popular, free Android apps. We found that we can replace many commonly used "dangerous" permissions with finer-grained permissions. Moreover, apps transformed to use these finer-grained permissions run largely as expected, with reasonable performance overhead